\begin{tabular}{c | c | c | c | c}
{\bf Method} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime}\\ \hline
PiFeNet \cite{le2022accurate} & 53.92 \% & 63.25 \% & 50.53 \% & 0.03 s / 1 core \\
CasA++ \cite{casa2022} & 53.84 \% & 60.14 \% & 51.35 \% & 0.1 s / 1 core \\
TED \cite{TED} & 53.48 \% & 60.13 \% & 50.89 \% & 0.1 s / 1 core \\
UPIDet \cite{zhang2023upidet} & 53.32 \% & 58.91 \% & 50.82 \% & 0.11 s / 1 core \\
EQ-PVRCNN \cite{Yang2022eqparadigm} & 52.81 \% & 61.73 \% & 49.87 \% & 0.2 s / GPU \\
VPFNet \cite{wang2021vpfnet} & 52.41 \% & 60.07 \% & 50.28 \% & 0.2 s / 1 core \\
Frustum-PointPillars \cite{paigwarhal03354114} & 52.23 \% & 60.98 \% & 48.30 \% & 0.06 s / 4 cores \\
LoGoNet \cite{li2023logonet} & 52.06 \% & 58.24 \% & 49.87 \% & 0.1 s / 1 core \\
TANet \cite{liu2019tanet} & 51.38 \% & 60.85 \% & 47.54 \% & 0.035s / GPU \\
CasA \cite{casa2022} & 51.37 \% & 57.95 \% & 49.08 \% & 0.1 s / 1 core \\
MLF-DET \cite{lin2023mlf} & 50.88 \% & 56.45 \% & 47.60 \% & 0.09 s / 1 core \\
MMLab PV-RCNN \cite{shi2020pv} & 50.57 \% & 59.86 \% & 46.74 \% & 0.08 s / 1 core \\
DPPFA-Net \cite{10308573} & 50.55 \% & 57.02 \% & 47.25 \% & 0.1 s / 1 core \\
HotSpotNet \cite{chen2020object} & 50.53 \% & 57.39 \% & 46.65 \% & 0.04 s / 1 core \\
VMVS \cite{ku2018joint} & 50.34 \% & 60.34 \% & 46.45 \% & 0.25 s / GPU \\
AVOD-FPN \cite{ku2018joint} & 50.32 \% & 58.49 \% & 46.98 \% & 0.1 s / \\
3DSSD \cite{yang3DSSD20} & 49.94 \% & 60.54 \% & 45.73 \% & 0.04 s / GPU \\
PointPainting \cite{vora2019pointpainting} & 49.93 \% & 58.70 \% & 46.29 \% & 0.4 s / GPU \\
SemanticVoxels \cite{fei2020semanticvoxels} & 49.93 \% & 58.91 \% & 47.31 \% & 0.04 s / GPU \\
ACDet \cite{acdet} & 49.82 \% & 58.35 \% & 47.17 \% & 0.05 s / 1 core \\
MMLab-PartA^2 \cite{shi2020part} & 49.81 \% & 59.04 \% & 45.92 \% & 0.08 s / GPU \\
USVLab BSAODet \cite{10052705} & 49.75 \% & 56.05 \% & 47.59 \% & 0.04 s / 1 core \\
ACFNet \cite{10363115} & 49.74 \% & 58.07 \% & 47.27 \% & 0.11 s / 1 core \\
F-PointNet \cite{qi2017frustum} & 49.57 \% & 57.13 \% & 45.48 \% & 0.17 s / GPU \\
F-ConvNet \cite{wang2019frustum} & 48.96 \% & 57.04 \% & 44.33 \% & 0.47 s / GPU \\
HVNet \cite{ye2020hvnet} & 48.86 \% & 54.84 \% & 46.33 \% & 0.03 s / GPU \\
CAT-Det \cite{zhang2022cat} & 48.78 \% & 57.13 \% & 45.56 \% & 0.3 s / GPU \\
STD \cite{std2019yang} & 48.72 \% & 60.02 \% & 44.55 \% & 0.08 s / GPU \\
PointPillars \cite{lang2018pointpillars} & 48.64 \% & 57.60 \% & 45.78 \% & 16 ms / \\
EPNet++ \cite{9983516} & 48.47 \% & 56.24 \% & 45.73 \% & 0.1 s / GPU \\
MGAF-3DSSD \cite{AnchorfreeMGASSD} & 48.46 \% & 56.09 \% & 44.90 \% & 0.1 s / 1 core \\
Fast-CLOCs \cite{Pang2022WACV} & 48.27 \% & 57.19 \% & 44.55 \% & 0.1 s / GPU \\
FromVoxelToPoint \cite{FromVoxelToPoint} & 48.15 \% & 56.54 \% & 45.63 \% & 0.1 s / 1 core \\
EOTL \cite{yang2023efficient} & 47.80 \% & 56.52 \% & 43.36 \% & TBD s / 1 core \\
HMFI \cite{li2022homogeneous} & 47.77 \% & 55.61 \% & 45.17 \% & 0.1 s / 1 core \\
LVFSD \cite{ERROR: Wrong syntax in BIBTEX file.} & 47.41 \% & 55.85 \% & 44.77 \% & 0.06 s / \\
P2V-RCNN \cite{P2VRCNN} & 47.36 \% & 54.15 \% & 45.10 \% & 0.1 s / 2 cores \\
Point-GNN \cite{shi2020pointgnn} & 47.07 \% & 55.36 \% & 44.61 \% & 0.6 s / GPU \\
3ONet \cite{10183841} & 47.05 \% & 56.76 \% & 44.62 \% & 0.1 s / 1 core \\
KPTr \cite{ERROR: Wrong syntax in BIBTEX file.} & 46.83 \% & 53.98 \% & 44.56 \% & 0.07 s / 1 core \\
SCNet \cite{8813061} & 46.73 \% & 56.87 \% & 42.74 \% & 0.04 s / GPU \\
PASS-PV-RCNN-Plus \cite{context} & 46.36 \% & 51.47 \% & 44.10 \% & 1 s / 1 core \\
VPA \cite{ERROR: Wrong syntax in BIBTEX file.} & 46.23 \% & 52.37 \% & 42.84 \% & 0.01 s / 1 core \\
MMLab-PointRCNN \cite{shi2019pointrcnn} & 46.13 \% & 54.77 \% & 42.84 \% & 0.1 s / GPU \\
ARPNET \cite{Ye2019} & 45.92 \% & 55.48 \% & 42.54 \% & 0.08 s / GPU \\
DSA-PV-RCNN \cite{bhattacharyya2021sadet3d} & 45.82 \% & 52.03 \% & 43.81 \% & 0.08 s / 1 core \\
SVGA-Net \cite{he2022svga} & 45.68 \% & 53.09 \% & 43.30 \% & 0.03s / 1 core \\
epBRM \cite{arxiv} & 45.49 \% & 52.48 \% & 42.75 \% & 0.10 s / 1 core \\
PG-RCNN \cite{koo2023pgrcnn} & 45.48 \% & 51.63 \% & 43.30 \% & 0.06 s / GPU \\
PDV \cite{PDV} & 45.45 \% & 51.95 \% & 43.33 \% & 0.1 s / 1 core \\
MLOD \cite{deng2019mlod} & 45.40 \% & 55.09 \% & 41.42 \% & 0.12 s / GPU \\
IA-SSD (single) \cite{zhang2022not} & 45.07 \% & 52.73 \% & 42.75 \% & 0.013 s / 1 core \\
DFAF3D \cite{tang2023dfaf3d} & 45.01 \% & 52.86 \% & 42.73 \% & 0.05 s / 1 core \\
SRDL \cite{ERROR: Wrong syntax in BIBTEX file.} & 44.84 \% & 52.42 \% & 42.56 \% & 0.05 s / 1 core \\
M3DeTR \cite{guan2021m3detr} & 44.78 \% & 50.63 \% & 42.57 \% & n/a s / GPU \\
SIF \cite{sif3d2d} & 44.28 \% & 52.05 \% & 42.03 \% & 0.1 s / 1 core \\
DVFENet \cite{HE2021} & 44.12 \% & 50.98 \% & 41.62 \% & 0.05 s / 1 core \\
Faraway-Frustum \cite{zhang2021faraway} & 43.85 \% & 52.15 \% & 41.68 \% & 0.1 s / GPU \\
S-AT GCN \cite{DBLPjournalscorrabs210308439} & 43.43 \% & 50.63 \% & 41.58 \% & 0.02 s / GPU \\
BirdNet+ \cite{barrera2021birdnet+} & 42.87 \% & 48.90 \% & 40.59 \% & 0.11 s / \\
L-AUG \cite{cortinhal2023semanticsaware} & 42.84 \% & 50.32 \% & 40.29 \% & 0.1 s / 1 core \\
IA-SSD (multi) \cite{zhang2022not} & 42.61 \% & 51.76 \% & 40.51 \% & 0.014 s / 1 core \\
XView \cite{xie2021xview} & 42.42 \% & 47.24 \% & 39.96 \% & 0.1 s / 1 core \\
GraphAlign(ICCV2023) \cite{song2023graphalign} & 41.95 \% & 46.61 \% & 40.05 \% & 0.03 s / GPU \\
PFF3D \cite{9340187} & 40.94 \% & 48.74 \% & 38.54 \% & 0.05 s / GPU \\
VSAC \cite{ERROR: Wrong syntax in BIBTEX file.} & 40.37 \% & 49.91 \% & 36.64 \% & 0.07 s / 1 core \\
DSGN++ \cite{chen2022dsgn++} & 38.92 \% & 50.26 \% & 35.12 \% & 0.2 s / \\
AB3DMOT \cite{Weng2019} & 38.79 \% & 47.51 \% & 35.85 \% & 0.0047s / 1 core \\
BirdNet+ (legacy) \cite{9294293} & 38.28 \% & 45.53 \% & 35.37 \% & 0.1 s / \\
CSW3D \cite{hu2019csw3d} & 37.96 \% & 49.27 \% & 33.83 \% & 0.03 s / 4 cores \\
StereoDistill \cite{liu2020tanet} & 37.75 \% & 50.79 \% & 34.28 \% & 0.4 s / 1 core \\
DMF \cite{chen2022DMF} & 34.92 \% & 42.08 \% & 32.69 \% & 0.2 s / 1 core \\
SparsePool \cite{wang2017fusing} & 34.15 \% & 43.33 \% & 31.78 \% & 0.13 s / 8 cores \\
MMLAB LIGA-Stereo \cite{Guo2021ICCV} & 34.13 \% & 44.71 \% & 30.42 \% & 0.4 s / 1 core \\
AVOD \cite{ku2018joint} & 33.57 \% & 42.58 \% & 30.14 \% & 0.08 s / \\
SparsePool \cite{wang2017fusing} & 33.22 \% & 41.55 \% & 29.66 \% & 0.13 s / 8 cores \\
CG-Stereo \cite{li2020confidence} & 29.56 \% & 39.24 \% & 25.87 \% & 0.57 s / \\
PointRGBNet \cite{Xie Desheng340} & 29.32 \% & 38.07 \% & 26.94 \% & 0.08 s / 4 cores \\
Disp R-CNN \cite{sun2020disprcnn} & 29.12 \% & 42.72 \% & 25.09 \% & 0.387 s / GPU \\
Disp R-CNN (velo) \cite{sun2020disprcnn} & 28.34 \% & 40.21 \% & 24.46 \% & 0.387 s / GPU \\
BirdNet \cite{BirdNet2018} & 23.06 \% & 28.20 \% & 21.65 \% & 0.11 s / \\
OC Stereo \cite{pon2020object} & 20.80 \% & 29.79 \% & 18.62 \% & 0.35 s / 1 core \\
YOLOStereo3D \cite{liu2021yolostereo3d} & 20.76 \% & 31.01 \% & 18.41 \% & 0.1 s / \\
DSGN \cite{Chen2020dsgn} & 20.75 \% & 26.61 \% & 18.86 \% & 0.67 s / \\
Complexer-YOLO \cite{Simon2019CVPRWorkshops} & 18.26 \% & 21.42 \% & 17.06 \% & 0.06 s / GPU \\
BKDStereo3D \cite{ERROR: Wrong syntax in BIBTEX file.} & 17.44 \% & 25.47 \% & 14.44 \% & 0.1 s / 1 core \\
BKDStereo3D w/o KD \cite{ERROR: Wrong syntax in BIBTEX file.} & 16.87 \% & 23.82 \% & 14.85 \% & 0.1 s / 1 core \\
TopNet-Retina \cite{8569433} & 14.57 \% & 18.04 \% & 12.48 \% & 52ms / \\
RT3D-GMP \cite{konigshof2020learning} & 14.22 \% & 19.92 \% & 12.83 \% & 0.06 s / GPU \\
MonoLTKD\_V3 \cite{ERROR: Wrong syntax in BIBTEX file.} & 13.62 \% & 19.79 \% & 11.92 \% & 0.04 s / 1 core \\
TopNet-HighRes \cite{8569433} & 13.50 \% & 19.43 \% & 11.93 \% & 101ms / \\
ESGN \cite{9869894} & 13.03 \% & 17.94 \% & 11.54 \% & 0.06 s / GPU \\
DD3D \cite{dd3d} & 12.51 \% & 18.58 \% & 10.65 \% & n/a s / 1 core \\
MonoLSS \cite{monolss} & 12.34 \% & 18.40 \% & 10.54 \% & 0.04 s / 1 core \\
PS-fld \cite{Chen2022CVPR} & 12.23 \% & 19.03 \% & 10.53 \% & 0.25 s / 1 core \\
CIE \cite{ye2022consistency} & 11.94 \% & 17.90 \% & 10.34 \% & 0.1 s / 1 core \\
OPA-3D \cite{su2023opa} & 11.01 \% & 17.14 \% & 9.94 \% & 0.04 s / 1 core \\
MonoUNI \cite{MonoUNI} & 10.90 \% & 16.54 \% & 9.17 \% & 0.04 s / 1 core \\
MonoDTR \cite{huang2022monodtr} & 10.59 \% & 16.66 \% & 9.00 \% & 0.04 s / 1 core \\
GUPNet \cite{lu2021geometry} & 10.37 \% & 15.62 \% & 8.79 \% & NA s / 1 core \\
CMKD \cite{YuHCMKDECCV2022} & 10.28 \% & 16.03 \% & 8.85 \% & 0.1 s / 1 core \\
DEVIANT \cite{kumar2022deviant} & 9.77 \% & 14.49 \% & 8.28 \% & 0.04 s / \\
MonoNeRD \cite{xu2023mononerd} & 9.66 \% & 15.27 \% & 8.28 \% & na s / 1 core \\
CaDDN \cite{CaDDN} & 9.41 \% & 14.72 \% & 8.17 \% & 0.63 s / GPU \\
SGM3D \cite{zhou2021sgm3d} & 9.39 \% & 15.39 \% & 8.61 \% & 0.03 s / 1 core \\
MonoRCNN++ \cite{MonoRCNNWACV23} & 9.04 \% & 13.45 \% & 7.74 \% & 0.07 s / GPU \\
HomoLoss(monoflex) \cite{Gu2022CVPR} & 8.81 \% & 13.26 \% & 7.41 \% & 0.04 s / 1 core \\
MonoDDE \cite{liu2020smoke} & 8.41 \% & 12.38 \% & 7.16 \% & 0.04 s / 1 core \\
Mix-Teaching \cite{Yang2022MixTeachingAS} & 8.40 \% & 12.34 \% & 7.06 \% & 30 s / 1 core \\
MDSNet \cite{xie2022mds} & 8.18 \% & 12.05 \% & 7.03 \% & 0.05 s / 1 core \\
LPCG-Monoflex \cite{peng2022lidar} & 7.92 \% & 12.11 \% & 6.61 \% & 0.03 s / 1 core \\
RefinedMPL \cite{vianney2019refinedmpl} & 7.92 \% & 13.09 \% & 7.25 \% & 0.15 s / GPU \\
Cube R-CNN \cite{brazil2023omni3d} & 7.65 \% & 11.67 \% & 6.60 \% & 0.05 s / GPU \\
MonoRUn \cite{monorun} & 7.59 \% & 11.70 \% & 6.34 \% & 0.07 s / GPU \\
MonoFlex \cite{monoflex} & 7.36 \% & 10.36 \% & 6.29 \% & 0.03 s / GPU \\
MonoPair \cite{chen2020cvpr} & 7.04 \% & 10.99 \% & 6.29 \% & 0.06 s / GPU \\
monodle \cite{MA2021CVPR} & 6.96 \% & 10.73 \% & 6.20 \% & 0.04 s / GPU \\
MonOAPC \cite{yao2023occlusion} & 6.82 \% & 9.62 \% & 5.78 \% & 0035 s / 1 core \\
TopNet-DecayRate \cite{8569433} & 6.59 \% & 8.78 \% & 6.25 \% & 92 ms / \\
Shift R-CNN (mono) \cite{shiftrcnn} & 5.66 \% & 8.58 \% & 4.49 \% & 0.25 s / GPU \\
FMF-occlusion-net \cite{liu2022fine} & 5.62 \% & 8.69 \% & 5.25 \% & 0.16 s / 1 core \\
Aug3D-RPN \cite{he2021aug3drpn} & 5.22 \% & 7.14 \% & 4.21 \% & 0.08 s / 1 core \\
TopNet-UncEst \cite{wirges2019capturing} & 4.60 \% & 6.88 \% & 3.79 \% & 0.09 s / \\
MonoPSR \cite{ku2019monopsr} & 4.56 \% & 7.24 \% & 4.11 \% & 0.2 s / GPU \\
DFR-Net \cite{dfr2021} & 4.52 \% & 6.66 \% & 3.71 \% & 0.18 s / \\
QD-3DT \cite{Hu2021QD3DT} & 4.23 \% & 6.62 \% & 3.39 \% & 0.03 s / GPU \\
M3D-RPN \cite{brazil2019m3drpn} & 4.05 \% & 5.65 \% & 3.29 \% & 0.16 s / GPU \\
DDMP-3D \cite{ddmp3d} & 4.02 \% & 5.53 \% & 3.36 \% & 0.18 s / 1 core \\
CMAN \cite{CMAN2022} & 3.96 \% & 5.24 \% & 3.18 \% & 0.15 s / 1 core \\
D4LCN \cite{ding2019learning} & 3.86 \% & 5.06 \% & 3.59 \% & 0.2 s / GPU \\
RT3DStereo \cite{Koenigshof2019Objects} & 3.65 \% & 4.72 \% & 3.00 \% & 0.08 s / GPU \\
MonoEF \cite{Zhou2021CVPR} & 3.05 \% & 4.61 \% & 2.85 \% & 0.03 s / 1 core \\
MonoLiG \cite{hekimoglu2023monocular} & 2.72 \% & 3.74 \% & 2.55 \% & 0.03 s / 1 core \\
SS3D \cite{DBLPjournalscorrabs190608070} & 2.09 \% & 2.48 \% & 1.61 \% & 48 ms / \\
SparVox3D \cite{9558880} & 2.05 \% & 2.90 \% & 1.69 \% & 0.05 s / GPU \\
PGD-FCOS3D \cite{PGD} & 1.88 \% & 2.82 \% & 1.54 \% & 0.03 s / 1 core \\
Plane-Constraints \cite{yao2023vertex} & 1.16 \% & 1.87 \% & 1.13 \% & 0.05 s / 4 cores \\
mBoW \cite{Behley2013IROS} & 0.00 \% & 0.00 \% & 0.00 \% & 10 s / 1 core
\end{tabular}